Evolutionary Ensemble Learning Pathloss Prediction for 4G and 5G Flying Base Stations With UAVs

The usage of unmanned aerial vehicles (UAVs) as flying base stations (FBSs) for expanding coverage and assisting the terrestrial cellular networks constitutes a promising technology for the fifth generation (5G) and beyond. A crucial parameter affecting cellular network design is path loss prediction. An alternative to the accurate, though time-consuming, propagation prediction with deterministic ray-tracing models could be machine learning (ML)-based predictions. Ensemble learning techniques are used in order to optimally combine the predictions of standalone models. That is, they combine the best-performing individual models into a better-performing meta-model. Our proposed method of the evolutionary tuned stacked ensemble optimizes the ensemble as a whole, instead of optimizing its individual base learners. To the best of our knowledge, this is the first time that an evolutionary technique is applied in order to mutually tune an ensemble’s base learners for a path loss modeling problem in electromagnetics. Moreover, we present a model that works in more than one frequency. As opposed to the standard implementation of ensemble learning, our method offers a significant performance boost with low complexity.